Adaptive filters are found in a wide range of applications and come in a wide variety of configurations each with distinctive properties. A particular configuration chosen may depend on specific properties needed for a target application. These properties, which include among others, rate of convergence, mis-adjustment, tracking, and computational requirements, are evaluated and weighed against each other to determine the appropriate configuration for the target application.
Of particular interest when choosing an adaptive filter configuration for use in a non-stationary signal environment are the rate of convergence, the mis-adjustment and the tracking capability. Good tracking capability is generally a function of the convergence rate and mis-adjustment properties of a corresponding algorithm. However, these properties may be contradictory in nature, in that a higher convergence rate will typically result in a higher convergence error or mis-adjustment of the resulting filter.
A recursive least squares (RLS) algorithm is generally a good tool for the non-stationary signal environment due to its fast convergence rate and low level of mis-adjustment. One particular form of the RLS algorithm is a recursive least squares lattice (RLSL) algorithm. The initial RLSL algorithm was introduced by Simon Haykin, and can be found in the “Adaptive Filter Theory Third Edition” book. The RLS class of adaptive filters exhibit fast convergence rates and are relatively insensitive to variations in an eigenvalue spread. Eigenvalues are a measure of correlation properties of the reference signal and the eigenvalue spread is typically defined as a ratio of the highest eigenvalue to the lowest eigenvalue. A large eigenvalue spread significantly slows down the rate of convergence for most adaptive algorithms.
However, the RLS algorithm typically requires extensive computational resources and can be prohibitive for embedded systems. Accordingly, there is a need to provide a mechanism by which the computational requirements of an RLSL structure adaptive filter are reduced.
Illustrative and exemplary embodiments of the invention are described in further detail below with reference to and in conjunction with the figures.